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We present Starduster, a supervised deep learning model that predicts themulti-wavelength SED from galaxy geometry parameters and star formation historyby emulating dust radiative transfer simulations. The model is comprised ofthree specifically designed neural networks, which take into account thefeatures of dust attenuation and emission. We utilise the Skirt radiativetransfer simulation to produce data for the training data of neural networks.Each neural network can be trained using ~ 4000 - 5000 samples. Compared withthe direct results of the Skirt simulation, our deep learning model produces ~0.005 mag and ~ 0.1 - 0.2 mag errors for dust attenuation and emissionrespectively. As an application, we fit our model to the observed SEDs ofIC4225 and NGC5166. Our model can reproduce the observations, and providereasonable measurements of the inclination angle and stellar mass. However,some predicted geometry parameters are different from an image-fitting study.Our analysis implies that including a constraint at (rest-frame) ~ 40 microncould alleviate the degeneracy in the parameter space for both IC4225 andNGC5166, leading to broadly consistent results with the image-fittingpredictions. Our SED code is publicly available and can be applied to bothSED-fitting and SED-modelling of galaxies from semi-analytic models.

Starduster: A multi-wavelength SED model based on radiative transfer simulations and deep learning